每日简报

fixest：快速且功能丰富的 R 计量估计框架AI 解读

fixest is an R package for fast and flexible econometric estimation. It provides a unified framework for applied research, with comprehensive support for a diverse class of models: ordinary least squares, instrumental variables, generalized linear models, maximum likelihood, and difference-in-differences. The package particularly excels at fixed-effects estimation, supported by a novel fixed-point acceleration algorithm implemented in C++. This algorithm achieves rapid…

基于贝叶斯决策理论的可扩展实验决策AI 解读

Randomized controlled experiments assess new policy impacts on performance metrics to inform launch decisions. Traditional approaches evaluate metrics independently despite correlations, and mixed results (e.g., positive revenue impact, negative customer experience) require manual judgment, hindering scalability. We propose a Bayesian decision-theoretic framework that systematically incorporates multiple objectives and trade-offs by comparing expected risks across decisions.…

信息几何驱动的高斯图模型序贯增长AI 解读

We investigate the properties of a class of regularisation-free approaches for Gaussian graphical inference based on the information-geometry-driven sequential growth of initially edgeless graphs. Relating the growth of a graph to a coordinate descent process, we characterise the fully-corrective descents corresponding to information-optimal growths, and propose numerically efficient strategies for their approximation. We demonstrate the ability of the proposed procedures to…

随机几何图上的图注意力网络节点回归：含Erdős–Rényi污染AI 解读

Graph attention networks (GATs) are widely used and often appear robust to noise in node covariates and edges, yet rigorous statistical guarantees demonstrating a provable advantage of GATs over non-attention graph neural networks~(GNNs) are scarce. We partially address this gap for node regression with graph-based errors-in-variables models under simultaneous covariate and edge corruption: responses are generated from latent node-level covariates, but only noise-perturbed…

稀疏Probit回归的均值场变分贝叶斯方法AI 解读

We consider Bayesian variable selection for binary outcomes under a probit link with a spike-and-slab prior on the regression coefficients. Motivated by the computational challenges encountered by Markov chain Monte Carlo (MCMC) samplers in high-dimensional regimes, we develop a mean-field variational Bayes approximation in which all variational factors admit closed-form updates, and the evidence lower bound is available in closed form. This, in turn, allows the development…

个体随机实地试验中常用估计量隐含的疫苗效力目标量AI 解读

We review vaccine efficacy (VE) estimands for susceptibility in individual randomized trials with natural (unmeasured) exposure, where individual responses are measured as time from vaccination until an event (e.g., disease from the infectious agent). Common VE estimands are written as $1-θ$, where $θ$ is some ratio effect measure (e.g., ratio of incidence rates, cumulative incidences, hazards, or odds) comparing outcomes under vaccination versus control. Although the ratio…

条件分位数对比的直接双重稳健估计AI 解读

Within heterogeneous treatment effect (HTE) analysis, various estimands have been proposed to capture the effect of a treatment conditional on covariates. Recently, the conditional quantile comparator (CQC) has emerged as a promising estimand, offering quantile-level summaries akin to the conditional quantile treatment effect (CQTE) while preserving some interpretability of the conditional average treatment effect (CATE). It achieves this by summarising the treated response…

货币统一、增长分化：采用欧元的动态效应AI 解读

Does euro adoption affect long-run economic growth? Existing evidence is mixed, reflecting limited treated countries, long horizons that challenge inference, and heterogeneity across member states. We estimate causal dynamic and heterogeneous treatment effects using Causal Forests with Fixed Effects (CFFE), a machine-learning approach that combines causal forests with two-way fixed effects. Under a conditional parallel-trends assumption, we find that euro adoption reduced…

通过函数型线性结构方程模型推断生物医学影像的因果效应AI 解读

Understanding the causal effects of organ-specific features from medical imaging on clinical outcomes is essential for biomedical research and patient care. We propose a novel Functional Linear Structural Equation Model (FLSEM) to capture the relationships among clinical outcomes, functional imaging exposures, and scalar covariates like genetics, sex, and age. Traditional methods struggle with the infinite-dimensional nature of exposures and complex covariates. Our FLSEM…

匹配机制的实验设计AI 解读

Matching mechanisms play a central role in operations management across diverse fields including education, healthcare, and online platforms. However, experimentally comparing a new matching algorithm against a status quo presents some fundamental challenges due to matching interference, where assigning a unit in one matching may preclude its assignment in the other. In this work, we take a design-based perspective to study the design of randomized experiments to compare two…

测量误差与分布迁移下的因果模仿学习AI 解读

We study offline imitation learning (IL) when part of the decision-relevant state is observed only through noisy measurements and the distribution may change between training and deployment. Such settings induce spurious state-action correlations, so standard behavioral cloning (BC) -- whether conditioning on raw measurements or ignoring them -- can converge to systematically biased policies under distribution shift. We propose a general framework for IL under measurement…

面板数据模型选择：Vuong检验的推广AI 解读

This paper generalizes the classical Vuong (1989) test to panel data models by employing modified profile likelihoods and the Kullback-Leibler information criterion. Unlike the standard likelihood function, the profile likelihood lacks certain regular properties, making modification necessary. We adopt a generalized panel data framework that incorporates group fixed effects for time and individual pairs, rather than traditional individual fixed effects. Applications of our…

多处理或析因处理观察研究中生存结果的倾向得分加权Cox回归AI 解读

In observational studies with survival or time-to-event outcomes, a propensity score weighted marginal Cox proportional hazard model with the treatment variable as the only predictor is commonly used to estimate the causal marginal hazard ratio between two treatments. Observational studies often have more than two treatments, but corresponding analysis methods are limited. In this paper, we combine the propensity score weighting method for multiple treatments and a marginal…

用支持向量机估计和预测二元选择模型AI 解读

The support vector machine (SVM) has an asymptotic behavior that parallels that of the quasi-maximum likelihood estimator (QMLE) for binary outcomes generated by a binary choice model (BCM), although it is not a QMLE. We show that, under the linear conditional mean condition for covariates given the systematic component used in the QMLE slope consistency literature, the slope of the separating hyperplane given by the SVM consistently estimates the BCM slope parameter, as…

约束下的政策学习：最大化主要结果并控制不良事件AI 解读

A medical policy aims to support decision-making by mapping patient characteristics to individualized treatment recommendations. Standard approaches typically optimize a single outcome criterion. For example, recommending treatment according to the sign of the Conditional Average Treatment Effect (CATE) maximizes the policy "value" by exploiting treatment effect heterogeneity. This point of view shifts policy learning towards the challenge of learning a reliable CATE…

观测研究中的最优样本拆分AI 解读

In observational studies of treatment effects, estimates may be biased by unmeasured confounders, which can potentially affect the validity of the results. Understanding sensitivity to such biases helps assess how unmeasured confounding impacts credibility. The design of an observational study strongly influences its sensitivity to bias. Previous work has shown that the sensitivity to bias can be reduced by dividing a dataset into a planning sample and a larger analysis…

时间序列数据中的动态因果推断AI 解读

We generalize the potential outcome framework to time series with an intervention by defining causal effects on stochastic processes. Interventions in dynamic systems alter not only outcome levels but also evolutionary dynamics -- changing persistence and transition laws. Our framework treats potential outcomes as entire trajectories, enabling causal estimands, identification conditions, and estimators to be formulated directly on path space. The resulting Dynamic Average…

是否采用欧元：欧元对贸易影响的异质性AI 解读

Two decades of research on the euro's trade effects have produced estimates ranging from 4% to 30%, with no consensus on the magnitude. We find evidence that this divergence may reflect genuine heterogeneity in the euro's trade effect across country pairs rather than methodological differences alone. Using Eurostat data on 15 EU countries (12 eurozone members plus Denmark, Sweden, and the UK as controls) from 1995-2015, we estimate that euro adoption increased bilateral…

含协变量的模糊断点回归设计：识别与估计AI 解读

We study fuzzy regression discontinuity designs with covariates and characterize the weighted averages of conditional local average treatment effects (WLATEs) that are point identified. Any identified WLATE equals a Wald ratio of conditional reduced-form and first-stage discontinuities. We highlight the Compliance-Weighted LATE (CWLATE), which weights cells by squared first-stage discontinuities and maximizes first-stage strength. For discrete covariates, we provide simple…

有限混合模型的偏差缩减估计及面板潜在组结构应用AI 解读

Finite mixture models are widely used in econometric analyses to capture unobserved heterogeneity. This paper shows that maximum likelihood estimation of finite mixtures of parametric densities can suffer from substantial finite-sample bias in all parameters under mild regularity conditions. The bias arises from the influence of outliers in component densities with unbounded or large support and increases with the degree of overlap among mixture components. I show that…

因果中介分析的四重稳健方法AI 解读

Estimating natural effects is a core task in causal mediation analysis. Existing triply robust (TR) frameworks (Tchetgen Tchetgen & Shpitser 2012) and their extensions have been developed to estimate the natural effects. In this work, we introduce a new quadruply robust (QR) framework that enlarges the model class for unbiased identification. We study two modeling strategies. The first is a nonparametric modeling approach, under which we propose a general QR estimator that…

因果偏好征询AI 解读

We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graphs (DAGs). From any black-box observational posterior, we model noisy expert judgments with a three-way likelihood over edge existence and direction. Posterior inference uses a flexible particle approximation, and queries are selected by an efficient expected information gain…

半参数有效的经验高阶影响函数估计量AI 解读

Robins et al. (2008, 2017) applied the theory of higher order influence functions (HOIFs) to derive an estimator of the mean $ψ$ of an outcome Y in a missing data model with Y missing at random conditional on a vector X of continuous covariates; their estimator, in contrast to previous estimators, is semiparametric efficient under the minimal conditions of Robins et al. (2009b), together with an additional (non-minimal) smoothness condition on the density g of X, because the…

断点差分设计：估计、推断与有效性检验AI 解读

This paper provides a formal econometric framework behind the newly developed difference-in-discontinuities design (DiDC). Despite its increasing use in applied research, there are currently limited studies of its properties. We formalize the theory behind the difference-in-discontinuity approach by stating the identification assumptions, proposing a nonparametric estimator, and deriving its asymptotic properties. We also provide comprehensive tests for one of the…